The Journal of Machine Learning Research
ACM SIGKDD Explorations Newsletter
Earlier Web usage statistics as predictors of later citation impact: Research Articles
Journal of the American Society for Information Science and Technology
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Topic evolution and social interactions: how authors effect research
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Unsupervised prediction of citation influences
Proceedings of the 24th international conference on Machine learning
BibNetMiner: mining bibliographic information networks
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Joint latent topic models for text and citations
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Popularity weighted ranking for academic digital libraries
ECIR'07 Proceedings of the 29th European conference on IR research
Estimating number of citations using author reputation
SPIRE'07 Proceedings of the 14th international conference on String processing and information retrieval
Proceedings of the 10th annual joint conference on Digital libraries
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Who should I cite: learning literature search models from citation behavior
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Citation count prediction: learning to estimate future citations for literature
Proceedings of the 20th ACM international conference on Information and knowledge management
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Recommending program committee candidates for academic conferences
Proceedings of the 2013 workshop on Computational scientometrics: theory & applications
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Usually scientists breed research ideas inspired by previous publications, but they are unlikely to follow all publications in the unbounded literature collection. The volume of literature keeps on expanding extremely fast, whilst not all papers contribute equal impact to the academic society. Being aware of potentially influential literature would put one in an advanced position in choosing important research references. Hence, estimation of potential influence is of great significance. We study a challenging problem of identifying potentially influential literature. We examine a set of hypotheses on what are the fundamental characteristics for highly cited papers and find some interesting patterns. Based on these observations, we learn to identify potentially influential literature via Future Influence Prediction (FIP), which aims to estimate the future influence of literature. The system takes a series of features of a particular publication as input and produces as output the estimated citation counts of that article after a given time period. We consider several regression models to formulate the learning process and evaluate their performance based on the coefficient of determination (R2). Experimental results on a real-large data set show a mean average predictive performance of 83.6% measured in R^2. We apply the learned model to the application of bibliography recommendation and obtain prominent performance improvement in terms of Mean Average Precision (MAP).